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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
### model ###
# input
with tf.name_scope('input') as scope:
x = tf.placeholder(tf.float32, [None, 28*28], name="input")
# a placeholder to hold the correct answer during training
labels = tf.placeholder(tf.float32, [None, 10], name="label")
# the probability of a neuron being kept during dropout
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
with tf.name_scope('model') as scope:
with tf.name_scope('fc1') as scope: # fc1 stands for 1st fully connected layer
# 1st layer goes from 784 neurons (input) to 500 in the first hidden layer
w1 = tf.Variable(tf.truncated_normal([28*28, 500], stddev=0.1), name="weights")
b1 = tf.Variable(tf.constant(0.1, shape=[500]), name="biases")
with tf.name_scope('softmax_activation') as scope:
# softmax activation
a1 = tf.nn.softmax(tf.matmul(x, w1) + b1)
with tf.name_scope('dropout') as scope:
# dropout
drop1 = tf.nn.dropout(a1, keep_prob)
with tf.name_scope('fc2') as scope:
# takes the first hidden layer of 500 neurons to 100 (second hidden layer)
w2 = tf.Variable(tf.truncated_normal([500, 100], stddev=0.1), name="weights")
b2 = tf.Variable(tf.constant(0.1, shape=[100]), name="biases")
with tf.name_scope('relu_activation') as scope:
# relu activation, and dropout for second hidden layer
a2 = tf.nn.relu(tf.matmul(drop1, w2) + b2)
with tf.name_scope('dropout') as scope:
drop2 = tf.nn.dropout(a2, keep_prob)
with tf.name_scope('fc3') as scope:
# takes the second hidden layer of 100 neurons to 10 (which is the output)
w3 = tf.Variable(tf.truncated_normal([100, 10], stddev=0.1), name="weights")
b3 = tf.Variable(tf.constant(0.1, shape=[10]), name="biases")
with tf.name_scope('logits') as scope:
# final layer doesn't have dropout
logits = tf.matmul(drop2, w3) + b3
with tf.name_scope('train') as scope:
with tf.name_scope('loss') as scope:
# loss function
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)
# use adam optimizer for training with a learning rate of 0.001
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
with tf.name_scope('evaluation') as scope:
# evaluation
correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# create a summarizer that summarizes loss and accuracy
tf.summary.scalar("Accuracy", accuracy)
# add average loss summary over entire batch
tf.summary.scalar("Loss", tf.reduce_mean(cross_entropy))
# merge summaries
summary_op = tf.summary.merge_all()
# create saver object
saver = tf.train.Saver()
### training ###
with tf.Session() as sess:
# initialize variables
# initialize summarizer filewriter
fw = tf.summary.FileWriter("/tmp/nn/summary", sess.graph)
# train the network
for step in range(20000):
batch_xs, batch_ys = mnist.train.next_batch(100), feed_dict={x: batch_xs, labels: batch_ys, keep_prob:0.2})
if step%1000 == 0:
acc =, feed_dict={
x: batch_xs, labels: batch_ys, keep_prob:1})
print("mid train accuracy:", acc, "at step:", step)
if step%100 == 0:
# compute summary using test data every 100 steps
summary =, feed_dict={
x: mnist.test.images, labels: mnist.test.labels, keep_prob:1})
# add merged summaries to filewriter,
# so they are saved to disk
fw.add_summary(summary, step)
# save trained model, "/tmp/nn/my_nn.ckpt")
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